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Critical Analysis of Advanced Hybrid Models for Mobile Phishing Detection Through Data Mining and Machine Learning

Author

Listed:
  • Karthik Vanna

    (VelTech University, India & International Center for AI and Cyber Security Research and Innovations, Asia University, Taichung, Taiwan)

  • Mosiur Rahaman

    (International Center for AI and Cyber Security Research and Innovations, Asia University, Taichung, Taiwan)

  • Akshat Gaurav

    (Ronin Institute, USA & International Center for AI and Cyber Security Research and Innovations, Asia University, Taichung, Taiwan)

  • Varsha Arya

    (Hong Kong Metropolitan University, Hong Kong & Center for Interdisciplinary Research, University of Petroleum and Energy Studies, Dehradun, India)

  • Ching-Hsien Hsu

    (Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan)

  • Brij B. Gupta

    (Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan & Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan & School of Cybersecurity, Korea University, Seoul, South Korea & UCRD, Chandigarh University, Chandigarh, India)

  • Kwok Tai Chui

    (Hong Kong Metropolitan University, Hong Kong)

Abstract

Phishing through mobiles is becoming advanced, attacking the users through malware applications, SMS, and social media. Dynamic threats better the conventional detection techniques, thereby hybrid approaches integrating machine learning, deep learning, and heuristic rules are the essentials. Here the work is on mobile security utilizing AI in interaction with 5G and edge computing for detection in real time. This survey discusses ensemble learning, federated learning, blockchain, and privacy-preserving techniques for defending against adversarial attacks and limited resources. It discusses elastic defences for mobiles and explores Explainable AI and quantum machine learning for enhanced performance and explainability. The results are from peer-reviewed journals and sources (2018-2024) like IEEE, Springer, and ScienceDirect, showing a modern overview of hybrid phishing detection.

Suggested Citation

  • Karthik Vanna & Mosiur Rahaman & Akshat Gaurav & Varsha Arya & Ching-Hsien Hsu & Brij B. Gupta & Kwok Tai Chui, 2025. "Critical Analysis of Advanced Hybrid Models for Mobile Phishing Detection Through Data Mining and Machine Learning," International Journal of Data Warehousing and Mining (IJDWM), IGI Global Scientific Publishing, vol. 21(1), pages 1-32, January.
    • Handle: RePEc:igg:jdwm00:v:21:y:2025:i:1:p:1-32
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